Speaker

Prof. Hernán Bruno

The global importance of online advertising and the new possibilities it enables call for a detailed understanding of its effects at the individual level. In this paper, we investigate consumer-specific response to online advertising repetitions and dynamics (i.e., timing) and its differential effectiveness across various online publishers. We develop a flexible model to accommodate different response shapes over ad “stock” and timing that parcels ad viewers into classes based on their overall patterns of response. The resulting classes show varying degrees of “wearout,” wherein additional exposures are decreasingly beneficial (in driving viewers to the advertiser’s website), including those who reach a point past which additional exposures have a negative marginal effect, which we refer to as “weariness.” The revealed classes are linked to members’ browsing behavior, profiled by distinct patterns of Internet usage. The model also captures differential efficacy effects: the most effective publisher is 10 times more so than the one just 40 places down. The analysis is carried out for online users who have been exposed to the target advertisement, and the results reveal a nontrivial proportion of visitors who arguably display “weariness” within the range of observed exposures.

Prof. S. Sriram

In recent years, many providers of news and entertainment have been exploring the possibility of monetizing online content. In the context of newspapers, the paywall instituted by the New York Times starting in March 2011 is a well-publicized case in point. While the premise behind paywalls is that the subscription revenue can potentially be a new source of income, the externalities that might arise as a consequence of the change in pricing are unclear. We study three potential externalities of newspaper paywalls and compare them gainst the new direct subscription revenue generated. The first two externalities that we consider are the effect of a paywall on the engagement of its online reader base, which likely impacts the newspaper’s advertising revenues; we term the latter the indirect effect of the paywall. Erection of the paywall can adversely affect the number of visitors to a newspaper’s website. This, in turn, can lower the quantity of ad impressions that can be served on the newspaper’s website. On the other hand, the newspaper is likely to have richer information on subscribing visitors, increasing its ability to serve targeted ads. Therefore, the paywall can potentially help a newspaper charge higher ad rates as a result of the improved quality of the served ad impressions. The net indirect effect of paywalls is likely to depend on the relative magnitudes of the changes in the quantity and quality of ad impressions subsequent to the paywall. The third externality is the spillover effect on the print version of the newspaper. If readers view print and online versions of a newspaper as substitutes, increasing the price of the latter is likely to increase the demand for the former. Moreover, many newspaper paywalls offer print subscribers free access to the online newspaper. Therefore, the value that a reader derives from the print subscription could be higher subsequent to the erection of the paywall. As a result, paywalls are likely to have a positive spillover effect on print subscription, and consequently, circulation. We document the sizes of the three externalities for the New York Times paywall and compare them with the direct subscription revenue generated. We comment on revenue implications for newspaper publishers from this increasingly popular mechanism to monetize digital content.

Prof. Stefan Wagner

Firms commonly run field experiments to improve their freemium pricing schemes. However, they often lack a framework for analysis that goes beyond directly measurable outcomes and focuses on longer term profit. We aim to fill this gap by structuring existing knowledge on freemium pricing into a stylized framework. We apply the proposed framework in the analysis of a field experiment that contrasts three variations of a freemium pricing scheme and comprises about 300,000 users of a software application. Our findings indicate that a reduction of free product features increases conversion as well as viral activity, but reduces usage – which is in line with the framework’s predictions. Additional back-of-the-envelope profit estimations suggest that managers were overly optimistic about positive externalities from usage and viral activity in their choice ofpricing scheme, leading them to give too much of their product away for free. Our framework and its exemplary application can be a remedy.

Prof. Cait Lamberton

The digital revolution of the last 15 years has, needless to say, been fertile ground for academic researchers. But what ground have we successfully covered, and what remains open to (or in need of) discovery? This presentation will first discuss the main findings of a critical analysis of research in digital, social and mobile academic marketing research published between 2001 and 2015. As part of this analysis, we will consider areas that appear to be oversaturated relative to industry need, areas of both connection and disconnection between academia and practice, and big questions that remain unanswered. In closing, I will then discuss new work that is attempting to explore this domain in new ways, ranging from policy applications to the sketching of the world of online social norms.

Prof. Enric Junqué de Fortuny

With the increasingly widespread collection and processing of ‘‘big data,’’ there is natural interest in using these data assets to improve decision making. One of the best understood ways to use data to improve decision making is via predictive analytics. In previous research, we found that when predictive models are built from low-level human behavior data we continue to see marginal increases in predictive performance even to very large scale. With organizations increasingly collecting data, it is surprising that only few organizations have actually incorporated this data into their predictive analytics despite their proven added value. Part of the reason lies in the idiosyncratic technical challenges of big data and the increased complexity of analyzing results coming from models built on such data. This talk discusses various ways to build models from fine-grained data and exposes how to unlock the underlying value in an easy and efficient way. Specifically, result from various publications will be shown, covering how to use network modeling techniques to increase predictive power in targeting applications in marketing, finance and frauddetection.